Our results must also be interpreted in light of model assumptions. First, we note that our model assumes linear network dynamics. While the brain is not a linear system, such simplified approximations can predict features of fMRI data [1]}, predict the control response of nonlinear systems of coupled oscillators [2]}, and more generally provide enhanced interpretability over nonlinear models. Nevertheless, considering control in nonlinear models of neural dynamics will constitute an important next step. Second, we consider a relaxed input matrix to ensure that state transitions are primarily influenced by the set of stimulating electrodes and to a lesser extent non-stimulating electrodes. This choice is not a true representation of single point control, but instead reflects the fact that the system is constantly modulated by endogenous sources [3]}, [4]}. Lastly, our model uses a time-invariant connectivity matrix. While DWI data is relatively stable over short time-scales, repeated stimulation can result in dynamic changes in plasticity [5]} that are not captured here.
